NDLI: White box sampling in uncertain data processing enabled by program analysis

Content Provider	ACM Digital Library
Author	Bao, Tao Zheng, Yunhui Zhang, Xiangyu
Abstract	Sampling is a very important and low-cost approach to uncertain data processing, in which output variations caused by input errors are sampled. Traditional methods tend to treat a program as a blackbox. In this paper, we show that through program analysis, we can expose the internals of sample executions so that the process can become more selective and focused. In particular, we develop a sampling runtime that can selectively sample in input error bounds to expose discontinuity in output functions. It identifies all the program factors that can potentially lead to discontinuity and hash the values of such factors during execution in a cost-effective way. The hash values are used to guide the sampling process. Our results show that the technique is very effective for real-world programs. It can achieve the precision of a high sampling rate with the cost of a lower sampling rate.
Starting Page	897
Ending Page	914
Page Count	18
File Format	PDF
ISSN	03621340 15581160
DOI	10.1145/2398857.2384681
Journal	ACM SIGPLAN Notices (SIGP)
Volume Number	47
Issue Number	10
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	1983-05-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Uncertainty Discrete factors Monte carlo Continuity Robustness Program analysis
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

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White box sampling in uncertain data processing enabled by program analysis